




'''simple docstring'''




class      UpperCAmelCase_     :


	"""simple docstring"""


	def __init__(						self						, UpperCAmelCase_				):
						snake_case_	=      arr.split(","				)







	def  _lowercase    (						self				):
						snake_case_	=      [int(self.array[0]				)] * len(self.array				)
						snake_case_	=      [int(self.array[0]				)] * len(self.array				)
						for i in range(1						, len(self.array				)				):
											snake_case_	=      max(
											    int(self.array[i]				) + sum_value[i - 1]						, int(self.array[i]				)				)
											snake_case_	=      max(sum_value[i]						, rear[i - 1]				)
						return rear[len(self.array				) - 1]


if __name__ == "__main__":
					lowercase__  							=					input('''please input some numbers:''')
					lowercase__  							=					SubArray(whole_array)
					lowercase__  							=					array.solve_sub_array()
					print(('''the results is:''', re))













'''simple docstring'''







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


__lowerCAmelCase        =							{"configuration_glpn": ["GLPN_PRETRAINED_CONFIG_ARCHIVE_MAP", "GLPNConfig"]}

try:
  if not is_vision_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __lowerCAmelCase        =							["GLPNFeatureExtractor"]
  __lowerCAmelCase        =							["GLPNImageProcessor"]

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __lowerCAmelCase        =							[
      "GLPN_PRETRAINED_MODEL_ARCHIVE_LIST",
      "GLPNForDepthEstimation",
      "GLPNLayer",
      "GLPNModel",
      "GLPNPreTrainedModel",
  ]


if TYPE_CHECKING:
  from .configuration_glpn import GLPN_PRETRAINED_CONFIG_ARCHIVE_MAP, GLPNConfig

  try:
    if not is_vision_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .feature_extraction_glpn import GLPNFeatureExtractor
    from .image_processing_glpn import GLPNImageProcessor

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_glpn import (
        GLPN_PRETRAINED_MODEL_ARCHIVE_LIST,
        GLPNForDepthEstimation,
        GLPNLayer,
        GLPNModel,
        GLPNPreTrainedModel,
    )


else:
  import sys

  __lowerCAmelCase        =							_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)




